Quick Links

How would you like to share?

Check out the newest gene microarray results in the Alzheimer's brain, and see if they give a lift to some of your favorite hypotheses and molecular suspects. The study, by Eric Blalock, Philip Landfield, and colleagues at the University of Kentucky, Lexington, appeared in this week's early online PNAS.

Blalock and colleagues were following up on last year's similar study looking at changes in gene expression in normal aging (Blalock, et al., 2003). Hoping to obtain sufficient statistical power to avoid both high false-positive and high false-negative errors, the scientists gathered a sizable sample of hippocampal tissue from AD patients with "incipient" (MMSE 20 - 26; n = 7), moderate (MMSE 14 - 19; n = 8), and severe disease (MMSE < 14; n = 7), along with nine elderly controls. In all, the authors found that the expression of 3,413 genes was significantly correlated with MMSE score and/or the neurofibrillary tangle (NFT) index across all subjects. With regard to early events in AD pathogenesis, the authors found that 609 of these genes correlated with incipient AD versus controls, and of these, changes in 89 genes correlated with both MMSE score and NFT index.

Last year, researchers led by Paul Coleman at the University of Rochester, New York, reported a microarray analysis of five AD brains which indicated that genes involved in trafficking synaptic vesicles were selectively decreased in AD (Yao et al., 2003). The year before, Walter Lukiw and colleagues at Louisiana State University in New Orleans reported microarray data of hippocampal CA1 from AD patients (Colangelo et al., 2002). These scientists found transcription and neurotrophic factors to be downregulated and apoptotic and proinflammatory signaling molecules to be upregulated in AD patients versus controls. Other studies have found evidence for gene expression changes in genes related to synaptic function and remodeling (see ARF related news story).

In an attempt to cull biological meaning from their new wealth of data, Blalock et al. have used software called the Expression Analysis Systematic Explorer (EASE), which was developed by researchers at the National Institute of Allergy and Infectious Disease (Hosack et al., 2003). They found "widespread and apparently orchestrated" changes in transcription factors/signaling genes regulating proliferation and differentiation, particularly upregulation of tumor suppressors, oligodendrocyte growth factors, and protein kinase A pathway molecules. They also point to upregulation of genes involved in cell adhesion, apoptosis, lipid metabolism, and initial inflammation processes, and downregulation of genes involved in protein folding/metabolism/transport, as well as some energy metabolism and signaling pathways, roughly mirroring and expanding the data of Colangelo et al.

This kind of unbiased data is frequently used to generate new hypotheses, and the scientists present a model for incipient AD pathology that starts in the white matter. "Alterations in axons or myelin sheaths initially stimulate growth/remyelination responses in localized oligodendrocytes, which in turn secrete growth factors which activate adjacent neurons and glial cells. This triggers compensatory tumor suppressor responses specific to cell type which induce protein aggregation, affect axonal-myelin interactions, and result in NFTs. As NFT density increases, wider extracellular matrix, amyloid precursor protein, and inflammatory changes may be triggered which impact cognition," the authors write. In particular, the authors suggest that this model could help explain the apparent progression of AD pathology along efferents from entorhinal cortex to hippocampus and neocortex, "leaving NFTs and plaques in its wake."—Hakon Heimer

Comments

Comments on this content

The manuscript by Blalock et al. is one of the most comprehensive and intriguing postmortem DNA microarray studies to date. Importantly, this manuscript goes beyond the “most changed gene” concept, and focuses on the transcript networks putatively affected in the hippocampus of subjects with AD. The authors build a convincing case that the observed, coordinated expression pattern changes represent molecular correlates of cognitive decline observed in AD.

The generated dataset is extremely valuable and of great interest to a number of researchers who have no access to postmortem brain tissue. The performed data analyses are sound but, just like all other approaches in the microarray field, have caveats. It will be interesting to test if different analytical approaches to this data reveal some currently unanticipated relationships. Despite the statistical arguments, microarray data validation with an independent method is strongly recommended in these studies. This dataset can live up to its full potential only if the critical gene expression changes are verified by qPCR, Northern or in-situ hybridization.

In summary, this study provides very few clear answers; rather, it focuses our attention to molecular pathways that should be more closely examined in the context of AD pathophysiology. The obtained data will have to be validated on a new cohort of subjects, the expression changes must be localized to precise cell types, and the implicated transcript networks should be examined across different brain regions. Furthermore, functional follow-up studies will have to provide validation for the activated oligodendrocyte model put forth in Figure 3. The impact of this manuscript on the field of AD research is too early to judge. If the proposed model holds up and if the presented data will serve as a basis for functional discoveries, this may become a landmark manuscript.

The comprehensive report by Eric Blalock et al. correlates gene expression levels garnered from cDNA microarray analysis of Alzheimer’s disease hippocampus with cognitive scores on the Mini Mental Status Examination (MMSE) and neurofibrillary tangle (NFT) counts across 31 people (nine controls, 22 AD patients with varying degrees of AD progression). Interestingly, the researchers identified many classes of transcripts that are regulated in what they deem “incipient AD,” or those with MMSE scores >20. Although it is somewhat unclear whether incipient AD is similar to the classification of mild cognitive impairment (MCI), without other global measures of neuropsychological testing it is quite possible that these two groups have considerable overlap. The researchers employed higher-order statistical analysis of gene expression using a package called the EASE program. This revealed upregulation of signaling markers and tumor suppressor genes, as well as oligodendrocyte growth factors, among others. Based upon these observations, the group put forth a provocative new model of AD pathogenesis, where tumor suppressor mediation and oligodendrocyte stimulation induces early axonopathy, which in turn spreads the disease across myelinated axons. This hypothesis is quite exciting, and whether it turns out to be realistic or not merits much more research.

I commend this group is for conducting a study which is complicated on several levels. First, the sheer number of genes being analyzed is tremendous. To correlate expression profiling with antemortem cognitive measures is quite remarkable and underlines the statistical prowess of the group. In addition, their model of AD pathogenesis was generated using cDNA microarray analysis as the principal technology. Microarray-based assessments have consistently been slighted because they are not “hypothesis-driven.” In contrast to this perception, the present report proposes a new mechanism of AD pathogenesis based upon cDNA array analysis in concert with clinicopathological correlations using MMSE scores and NFT counts. The manuscript is groundbreaking work in this regard, especially as it relates to hypothesis-driven science. One drawback is that these data are based solely on cDNA microarray analysis without independent validation from alternative cellular and molecular methodologies. Presumably, these crucial studies will be performed at a later point (e.g., qPCR, Northern analysis, etc.).

In summary, the authors provide evidence for the regulation of many classes of transcripts, including tumor suppressors and oligodendrocyte lineage genes, as well as involvement of cell adhesion, apoptosis, and lipid metabolism-based genes using RNA extracted postmortem from the hippocampus of normal and AD patients. The authors have presented credible evidence for a hypothesis of AD progression occurring through axonopathy and spreading along myelinated axons. Since these results were garnered using regional hippocampal dissections with an admixture of many neuronal and non-neuronal cell types, it is tempting to speculate what the expression profile would be in this same cohort for individual populations of vulnerable neurons, such as CA1 pyramidal cells, and whether their hypothesis could be borne out at the single-cell level.

In this study, the authors used Affymetrix arrays to examine transcript expression in hippocampal CA1 of 31 cases, spanning the range from controls > “incipient” AD > moderate > severe AD. Each of 9,921 transcripts was correlated with NFT “scores," and/or MMSE scores. Thus, almost 20,000 correlation coefficients were computed. The false discovery rate was calculated as ~0.20. Out of the 9,921 transcripts, 3,413 were determined to be correlated with NFT and/or MMSE. Of these, 1,977 were upregulated, while 1,436 were downregulated. This finding of more upregulated transcripts is consistent with data from other array studies. However, a number of studies have found an overall decrease in transcript level in AD. These two findings can be reconciled by assuming that increased expression of upregulated genes is insufficient to overcome the decreased expression of downregulated genes. In general, many of the usual suspects were identified in the authors’ data.

A separate analysis of the “incipient” AD cases (marginal NFT and MMSE scores) yielded interesting differences between these early AD cases and more advanced AD. One that is stressed by the authors is overrepresentation in early AD of downregulation of transcripts related to protein processing. In fact, the authors refer to “the apparent collapse of protein processing machinery so early in the disease.” This finding is consistent with the data produced by the Nixon-Cataldo team over the years, which demonstrate swelling of endosomes and upregulation of lysosomal enzymes early in the course of AD.

The authors have dealt with a number of issues inherent in array studies of AD. For example, it is well-known that many nondemented “control” cases may have significant AD pathology. The authors refrained from defining cases according to diagnostic categories by ignoring clinical diagnosis and using MMSE and NFT density as independent variables. This has served these authors well, as it has in a small number of other studies (e.g., Coleman et al., 1992).

Another issue in array studies implicitly dealt with by the authors is the way in which array data are used. Arrays may be used either 1) to examine a predefined subset of gene products in order to test specific hypotheses or 2) as a broad survey with the aim of using the resulting data to define hypotheses. Although Study Sections are likely to dismiss survey proposals with the pejorative term “fishing expedition,” these surveys can have considerable utility. In the case of the survey approach, one of the criteria for success must be: Has the author used the resulting data to generate specific, testable hypotheses? The authors of the present paper satisfy this criterion extremely well, for they use their array data to define a specific model of a cascade of early events in the progression of AD, a model that emphasizes a central role for oligodendroglia. I hope that this model will lead to studies directed at explicitly testing its validity.

A third issue in array studies is the establishment of validity of the data. In the opinion of this reviewer, the gold standard for validity of array findings for individual transcripts is real-time quantitative RT-PCR. This is an impossible standard for most laboratories when dealing with the numbers of genes involved here. In a much smaller sample, we have found quantitative array data to be validated by RT-PCR for about 50 percent of the transcripts examined (Therianos et al., Amer. J. Pathol., 2004). Blalock et al. have adopted an alternate criterion, which may be called internal consistency. This is essentially finding “coregulation of genes within related pathways and categories.” Although not as stringent as quantitative RT-PCR with respect to individual transcripts, satisfaction of this criterion, along with the presence of expected findings, gives added confidence in the validity of the new findings reported here.